Date of Completion
Longitudinal Data, Bayesian Joint Model, Alzheimer's Disease, SOMI, Transitional Model, High Dimensional Data, Human Microbiome Project
Field of Study
Doctor of Philosophy
We propose three statistical methods for longitudinal ordinal outcomes and categorical outcomes. The proposed Bayesian joint model of longitudinal ordinal outcome and time-to-event data was applied to dementia data to evaluate the prediction accuracy of a staging model for memory impairment. The second method, the Bayesian stochastic model for multistage can be used to measure the correlation across different transition processes. This model was applied to the Human Microbiome Project (HMP) data to investigate the effect of baseline covariates on the change of microbiome type and evaluate the correlation of change in microbiome type and change in healthy status. The third method, the transitional model for high dimensional data is able to detect the significant covariates for multistage transitions. We applied this approach to the HMP data as well and we investigated the effect of microbiome genus data on the transition of health status.
Qi, Qi, "Statistical Methods for Longitudinal Data with Applications to Dementia and Human Microbiome Projects" (2020). Doctoral Dissertations. 2619.
Available for download on Friday, August 08, 2025